Automatic deep learning-based myocardial infarction segmentation from delayed enhancement MRI

A hybrid segmentation and classification CAD framework for automated myocardial infarction prediction from MRI images

Early diagnosis of myocardial infarction (MI) is critical for preserving cardiac function and improving patient outcomes through timely intervention. This study proposes an annovaitive computer-aided diagnosis (CAD) system for the simultaneous segmentation and classification of MI using MRI images. The system is evaluated under two primary approaches: a serial approach, where segmentation is first applied to extract image patches for subsequent classification, and a parallel approach, where segmentation and classification are performed concurrently using full MRI images. The multi-class segmentation model identifies four key heart regions: left ventricular cavity (LV), normal myocardium (Myo), myocardial infarction (MI), and persistent microvascular obstruction (MVO). The classification stage employs three AI-based strategies: a single deep learning model, feature-based fusion of multiple AI models, and a hybrid ensemble model incorporating the Vision Transformer (ViT). Both segmentation and classification models are trained and validated on the EMIDEC MRI dataset using five-fold cross-validation. The adopted ResU-Net achieves high F1-scores for segmentation: 91.12% (LV), 88.39% (Myo), 80.08% (MI), and 68.01% (MVO). For classification, the hybrid CNN-ViT model in the parallel approach demonstrates superior performance, achieving 98.15% accuracy and a 98.63% F1-score. These findings highlight the potential of the proposed CAD system for real-world clinical applications, offering a robust tool to assist healthcare professionals in accurate MI diagnosis, improved treatment planning, and enhanced patient care.

Artificial Intelligence in Imaging for Personalized Management of Coronary Artery Disease

The precision of imaging and the number of other risk-assessing and diagnostic methods are constantly growing, allowing for the uptake of additional strategies for individualized therapies. Personalized medicine has the potential to deliver more adequate treatment, resulting in better clinical outcomes, based on each patient's vulnerability or genetic makeup. In addition to increased efficiency, costs related to this type of procedure can be significantly lower. Useful assistance in designing individual therapies may be assured by the adoption of artificial intelligence (AI). Recent years have brought essential developments in deep and machine learning techniques. Advances in technologies such as convolutional neural networks (CNNs) have enabled automatic analyses of images, numerical data, and video data, providing high efficiency in the creation of prediction models. The number of AI applications in medicine is constantly growing, and the effectiveness of these techniques has been demonstrated in coronary computed tomography angiography (CCTA), optical coherence tomography (OCT), and many others. Moreover, AI models may be useful in direct therapy optimization for patients with coronary artery disease (CAD), who are burdened with high risk. The combination of well-trained AI with the design of individual treatment pathways can lead to improvements in health care. However, existing limitations, such as non-adapted guidelines or the lack of randomized clinical trials to evaluate AI's true accuracy, may contribute to delays in introducing automatic methods into practical use. This review critically appraises the developed tools that are potentially useful for clinicians in guiding personalized patient management, as well as current trials in this field.

Deep learning for cardiac imaging: focus on myocardial diseases, a narrative review

The integration of computational technologies into cardiology has significantly advanced the diagnosis and management of cardiovascular diseases. Computational cardiology, particularly, through cardiovascular imaging and informatics, enables a precise diagnosis of myocardial diseases utilizing techniques such as echocardiography, cardiac magnetic resonance imaging, and computed tomography. Early-stage disease classification, especially in asymptomatic patients, benefits from these advancements, potentially altering disease progression and improving patient outcomes. Automatic segmentation of myocardial tissue using deep learning (DL) algorithms improves efficiency and consistency in analyzing large patient populations. Radiomic analysis can reveal subtle disease characteristics from medical images and can enhance disease detection, enable patient stratification, and facilitate monitoring of disease progression and treatment response. Radiomic biomarkers have already demonstrated high diagnostic accuracy in distinguishing myocardial pathologies and promise treatment individualization in cardiology, earlier disease detection, and disease monitoring. In this context, this narrative review explores the current state of the art in DL applications in medical imaging (CT, CMR, echocardiography, and SPECT), focusing on automatic segmentation, radiomic feature phenotyping, and prediction of myocardial diseases, while also discussing challenges in integration of DL models in clinical practice.

Deep learning segmentation model for quantification of infarct size in pigs with myocardial ischemia/reperfusion

Infarct size (IS) is the most robust end point for evaluating the success of preclinical studies on cardioprotection. The gold standard for IS quantification in ischemia/reperfusion (I/R) experiments is triphenyl tetrazolium chloride (TTC) staining, typically done manually. This study aimed to determine if automation through deep learning segmentation is a time-saving and valid alternative to standard IS quantification. High-resolution images from TTC-stained, macroscopic heart slices were retrospectively collected from pig experiments (n = 390) with I/R without/with cardioprotection to cover a wide IS range. Existing IS data from pig experiments, quantified using a standard method of manual and subsequent digital labeling of film-scan annotations, were used as reference. To automate the evaluation process with the aim to be more objective and save time, a deep learning pipeline was implemented; the collected images (n = 3869) were pre-processed by cropping and labeled (image annotations). To ensure their usability as training data for a deep learning segmentation model, IS was quantified from image annotations and compared to IS quantified using the existing film-scan annotations. A supervised deep learning segmentation model based on dynamic U-Net architecture was developed and trained. The evaluation of the trained model was performed by fivefold cross-validation (n = 220 experiments) and testing on an independent test set (n = 170 experiments). Performance metrics (Dice similarity coefficient [DSC], pixel accuracy [ACC], average precision [mAP]) were calculated. IS was then quantified from predictions and compared to IS quantified from image annotations (linear regression, Pearson's r; analysis of covariance; Bland-Altman plots). Performance metrics near 1 indicated a strong model performance on cross-validated data (DSC: 0.90, ACC: 0.98, mAP: 0.90) and on the test set data (DSC: 0.89, ACC: 0.98, mAP: 0.93). IS quantified from predictions correlated well with IS quantified from image annotations in all data sets (cross-validation: r = 0.98; test data set: r = 0.95) and analysis of covariance identified no significant differences. The model reduced the IS quantification time per experiment from approximately 90 min to 20 s. The model was further tested on a preliminary test set from experiments in isolated, saline-perfused rat hearts with regional I/R without/with cardioprotection (n = 27). There was also no significant difference in IS between image annotations and predictions, but the performance on the test set data from rat hearts was lower (DSC: 0.66, ACC: 0.91, mAP: 0.65). IS quantification using a deep learning segmentation model is a valid and time-efficient alternative to manual and subsequent digital labeling.

Constantly optimized mean teacher for semi-supervised 3D MRI image segmentation

The mean teacher model and its variants, as important methods in semi-supervised learning, have demonstrated promising performance in magnetic resonance imaging (MRI) data segmentation. However, the superior performance of teacher model through exponential moving average (EMA) is limited by the unreliability of unlabeled image, resulting in potentially unreliable predictions. In this paper, we propose a framework to optimized the teacher model with reliable expert-annotated data while preserving the advantages of EMA. To avoid the tight coupling that results from EMA, we leverage data augmentations to provide two distinct perspectives for the teacher and student models. The teacher model adopts weak data augmentation to provide supervision for the student model and optimizes itself with real annotations, while the student uses strong data augmentation to avoid overfitting on noise information. In addition, double softmax helps the model resist noise and continue learning meaningful information from the images, which is a key component in the proposed model. Extensive experiments show that the proposed method exhibits competitive performance on the Left Atrium segmentation MRI dataset (LA) and the Brain Tumor Segmentation MRI dataset (BraTS2019). For the LA dataset, we achieved a dice of 91.02% using only 20% labeled data, which is close to the dice of 91.14% obtained by the supervised approach using 100% labeled data. For the BraTs2019 dataset, the proposed method achieved 1.02% and 1.92% improvement on 5% and 10% labeled data, respectively, compared to the best baseline method on this dataset. This study demonstrates that the proposed model can be a potential candidate for medical image segmentation in semi-supervised learning scenario.

Deep learning for automatic volumetric segmentation of left ventricular myocardium and ischaemic scar from multi-slice late gadolinium enhancement cardiovascular magnetic resonance

AIMS

This study details application of deep learning for automatic volumetric segmentation of left ventricular (LV) myocardium and scar and automated quantification of myocardial ischaemic scar burden from late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR).

METHODS AND RESULTS

We included 501 images and manual segmentations of short-axis LGE-CMR from over 20 multinational sites, from which 377 studies were used for training and 124 studies from unique participants for internal validation. A third test set of 52 images was used for external evaluation. Three models, U-Net, Cascaded U-Net, and U-Net++, were trained with a novel adaptive weighted categorical cross-entropy loss function. Model performance was evaluated using concordance correlation coefficients (CCCs) for LV mass and per cent myocardial scar burden. Cascaded U-Net was found to be the best model for the quantification of LV mass and scar percentage. The model exhibited a mean difference of -5 ± 23 g for LV mass, -0.4 ± 11.2 g for scar mass, and -0.8 ± 7% for per cent scar. CCC were 0.87, 0.77, and 0.78 for LV mass, scar mass, and per cent scar burden, respectively, in the internal validation set and 0.75, 0.71, and 0.69, respectively, in the external test set. For segmental scar mass, CCC was 0.74 for apical scar, 0.91 for mid-ventricular scar, and 0.73 for basal scar, demonstrating moderate to strong agreement.

CONCLUSION

We successfully trained a convolutional neural network for volumetric segmentation and analysis of LV scar burden from LGE-CMR images in a large, multinational cohort of participants with ischaemic scar.

Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years

BACKGROUND

Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology.

RESULTS

We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology.

CONCLUSIONS

The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.

The Role of Artificial Intelligence in Cardiac Imaging

Artificial intelligence (AI) is having a significant impact in medical imaging, advancing almost every aspect of the field, from image acquisition and postprocessing to automated image analysis with outreach toward supporting decision making. Noninvasive cardiac imaging is one of the main and most exciting fields for AI development. The aim of this review is to describe the main applications of AI in cardiac imaging, including CT and MR imaging, and provide an overview of recent advancements and available clinical applications that can improve clinical workflow, disease detection, and prognostication in cardiac disease.

Application of artificial intelligence techniques for automated detection of myocardial infarction: a review

Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals worldwide. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG and some other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and some other biophysical signals.

Prediction of Myocardial Infarction From Patient Features With Machine Learning

This study proposes machine learning-based models to automatically evaluate the severity of myocardial infarction (MI) from physiological, clinical, and paraclinical features. Two types of machine learning models are investigated for the MI assessment: the classification models classify the presence of the infarct and the persistent microvascular obstruction (PMO), and the regression models quantify the Percentage of Infarcted Myocardium (PIM) of patients suspected of having an acute MI during their reception in the emergency department. The ground truth labels for these supervised models are derived from the corresponding Delayed Enhancement MRI (DE-MRI) exams and manual annotations of the myocardium and scar tissues. Experiments were conducted on 150 cases and evaluated with cross-validation. Results showed that for the MI (PMO inclusive) and the PMO (infarct exclusive), the best models obtained respectively a mean error of 0.056 and 0.012 for the quantification, and 88.67 and 77.33% for the classification accuracy of the state of the myocardium. The study of the features' importance also revealed that the troponin value had the strongest correlation to the severity of the MI among the 12 selected features. For the proposal's translational perspective, in cardiac emergencies, qualitative and quantitative analysis can be obtained prior to the achievement of MRI by relying only on conventional tests and patient features, thus, providing an objective reference for further treatment by physicians.